專題03 閱讀理解（說明文）
（2024·廣東廣州·二模）Artists everywhere are getting “understandably nervus” abut recent advances in artificial intelligence. Last mnth, a winner f an art prize at the Clrad State Fair “sparked a vilent prtest” when he psted the news and explained that he’d created his image using an AI prgram. Critics quickly accused 39-year-ld Lance Allen f cheating. T be fair, Allen had wn in the digital art categry and made n secret f hw the image had been prduced. But the rules f art making are clearly changing.
Allen’s creative prcess, t be clear, “was nt a push-buttn peratin, ”said Jasn Blain in Frbes. He claims t have spent 80 hurs n his entry, first n fine-tuning his text prmpts (提示), then by tuching up the final image using Phtshp and similar tls, then arranging t print the image n canvas. He made the finished prduct using AI much as a phtgrapher creates an image using a camera.
But Allen, a tabletp game develper, is awed by AI’s capabilities and urges artists and illustratrs t welcme the technlgy rather than fight it. “Art is dead,” he says. “AI wn. Humans lst.” A mre inspiring lessn t take frm his victry, thugh, is that image generatrs are likely t “expand the appreciatin fr and creatin f art” by pening the field t peple, like him, wh culd never draw anything as detailed as his award-winning image. “If anything, we will have mre artists,” and as the technlgy prgresses, “we might see the emergence f art styles that nne have seen befre.”
Yu can’t blame traditinal artists if they’re unhappy. Image generatrs wrk their magic, after all, by analyzing the aesthetics (美學(xué)) f millins f pre-existing images. One f the mst cmplicated image generatrs “makes crystal clear just hw destructive this technlgy will be,” said Lz Elit in New Atlas. Given a specific prmpt, it can prduce an image f just abut anything yu can imagine and even fllw the style f a favrite artist’s wrk. Its arrival marks “an incredible ppularizatin f visual creativity” while aiming “a knife t the heart f anyne wh’s spent decades imprving their artistic techniques hping t make a living frm them.”
1．Why are artists getting nervus abut AI recently?
A．A winner f an art prize used AI.B．Lance Allen cheated in the art cmpetitin.
C．The digital art will sn dminate.D．There will be great changes in art creatin.
2．What des the authr intend t tell us in paragraph 2?
A．It was n easy wrk fr Allen even with Al.B．Allen wrked as a phtgrapher creating an image.
C．AI played a key rle in Allen’s art creatin.D．Althugh with AI, Allen’s creatin cunted a lt.
3．What lessn can we draw frm Allen’s winning?
A．Human has been beaten by AI.B．AI will make art mre ppular.
C．Greater artists and new art styles will appear.D．AI enables amateurs t win art cmpetitins.
4．Why des Lz Elit say the new technlgy will be destructive?
A．It wrks by analyzing images created by human.
B．It can prduce images beynd peple’s imaginatin.
C．It makes artists’ lng-time effrt meaningless.
D．It makes it impssible fr artists t make a living.
（2024·浙江·二模）Fr want f a chip, the factry was lst. On May 18th Tyta became the latest carmaker frced t cut prductin in a glbal shrtage f micrchips, annuncing it wuld stp wrk at tw f its plants in Japan. Other car makers have als had t send wrkers hme.
The pain is nt limited t the car industry, fr the shrtage spans all srts f chips, frm the expensive, high-tech devices that pwer smartphnes and data-centers t the simple sensrs and micr-cntrllers that have becme a vital cmmdity (商品). This chip drught is the result f the cvid-19 pandemic interacting with an industry that is subject t cycles f bm and bust.
“The mst imprtant thing”, says Malclm Penn, wh runs a chip-industry cnsultancy, “is that shrtages are a natural part f the industry.” Chipmaking is a gd example f a “prk-cycle” business, named fr the regular swings between under- and ver-supply in prk markets. As with pigs, the supply f chips cannt quickly react t changes in demand. Capacity was tight even befre the pandemic, says Mr Penn, pinting ut that investment by chipmakers in factry equipment has been belw its lng-term average fr many years.
The pandemic arrived at the wrst pssible time. After an early crash, demand in several fields bmed. Lcked-dwn cnsumers bught laptps and ther devices. Clud-cmputing peratrs added servers t deal with the wave f hme-wrkers. The car industry was particularly badly hit by a decisin t cut rders early in the pandemic-demand fr cars has since recvered. But the cmplexities f the prductin prcess mean it takes time t recver. “I can cancel my rders in an afternn,” says Mr. Penn. “If I want t start them up again, that takes mnths--and that capacity is nw busy serving ther custmers.”
But the prk cycle is turning nce again. Taiwan Semicnductr Manufacturing Cmpany, the wrld’s biggest chipmaker, plans t spend $30bn n new capacity this year. Tw ther giants, have als decided n further investment. That will bring relief t the wider ecnmy, but nt immediately. The bss f IBM said he thught the shrtages might last fr tw years. And, says Mr Penn, when the drught eventually ends, chipmakers may find they face a familiar prblem n a bigger scale: a capacity investment in respnse t serius shrtages tday culd well mean a sizeable surplus (過剩) tmrrw.
5．Hw des the authr illustrate the cyclical nature f chipmaking in paragraph 3?
A．By referring t a qutatin.B．By making a cmparisn.
C．By drawing a cnclusin.D．By presenting an argument.
6．What may the investment mentined in paragraph 5 eventually lead t?
A．Imprved supply chain stability.B．Timely assistance t the business.
C．Ecnmic grwth in related sectrs.D．Pssible future versupply f chips.
7．What can we learn frm the passage?
A．Prductin capacity wuld recver sn.
B．A cmbinatin f reasns led t chip shrtage.
C．An investment f S30bn was enugh fr the prblem.
D．Tyta was the first carmaker t suspend prductin.
8．What might be the best title fr the passage?
A．Lading, please waitB．Dying, please act
C．Calling, please respndD．Over-supplying, please stp
（2024·浙江金華·二模）Smetimes we nly appreciate smething when we realize we may lse it. That is the stry f the Everglades. A shallw slw-mving river, the Everglades nce cvered abut 18,000 square miles f suthern Flrida. Until the 1900s, few peple lived in the grassy wetlands. Nt much was understd abut the unique balance f nature that existed there. Plants, creatures, and water had frmed a remarkable ecsystem.
By the early 1900s, Flrida’s pleasant winters attracted mre peple. Visitrs became new permanent residents. They built hmes and rads. The cnditins lked gd fr farming, s the newcmers planted large agricultural crps. But Suth Flrida’s cycle f flding was a prblem. T address that, develpers attempted t drain (排水) the land. They als built structures t cntrl water levels and flw.
Thse changes made it easier fr mre peple t live year-rund in Suth Flrida. Hwever, they als disturbed life in the Everglades, which depends n freshwater regularly refilling the land. The area’s grwing human ppulatin needed freshwater. And large farms cnsumed large quantities f freshwater. By the mid-1900s, water levels in suthern Flrida began t g dwn. Lack f freshwater wasn’t the nly prblem. As mre and mre land was develped fr peple and farms, the Everglades’ histric bundaries cntracted. Lss f habitat and hunting threatened the survival f native species in the Everglades.
Sme peple hped that the gvernment’s recgnitin might save the Everglades. They fught fr it. Everglades Natinal Park was established in 1947. It became the first park in the United States created fr its bidiversity.
Nw, Everglades Natinal Park prtects 1.5 millin acres alng the suthern tip f Flrida. An amazing variety f creatures live there. Abut 360 different species f birds have been sighted in the park. Nearly 300 different species f fish have been identified. Abut 40 species f mammals and 50 species f reptiles inhabit the park. Nature still rules in the Everglades, a place wrth understanding, appreciating, and prtecting.
9．What was the Everglades like befre the 1900s?
A．Naturally wild.B．Partly explred.
C．Cmpletely lifeless.D．Thickly ppulated.
10．What prblem did new residents cause fr the Everglades?
A．A cycle f flding.
B．Pllutin f freshwater.
C．Pssible extinctin f native species.
D．The extensin f histric bundaries.
11．Why are the figures mentined in the last paragraph?
A．T attract visitrs t the park.
B．T stress the great pwer f nature.
C．T call fr mre effrts t prtect nature.
D．T shw the successful cnservatin f the park.
12．What is the text mainly abut?
A．Hw peple adapted t life in the Everglades.
B．Hw Everglades Natinal Park was established.
C．Hw humans harmed and saved the Everglades.
D．Hw the ecsystem f the Everglades was frmed.
（2024·浙江杭州·二模）Educatin in 2080 is distinctive frm educatin in the 2020s. Until abut 2035, the main functin f educatin systems was t supply the ecnmy with the next generatin f wrkers. In 2080, the purpse f educatin is the well-being f sciety and all its members. T make this a bit mre tangible fr yu, I wuld like t give an example f what a child’s educatin lks like in 2080. Her name is Shemsy. Shemsy is 13, and she is cnfident and lves learning.
Shemsy des nt g t schl in the mrning because schls as yu knw them n lnger exist. The institutin was ablished as it was widely thught f as mre like a prisn r a factry than a creative learning envirnment. Schls have been replaced with “Learning Hubs” that are nt restricted t certain ages. They are where intergeneratinal learning happens, in line with the belief that learning is a lifelng pursuit.
Every year, Shemsy designs her learning jurney fr the year with a highly attentive “teacher-citizen”. Shemsy is actively engaged in designing her educatin and has t prpse prjects she wuld like t be invlved in t cntribute t and serve her cmmunity. She als spends lts f time playing as the rle f play in learning has finally been recgnized as essential and cre t ur humanity. Shemsy wrks a lt cllabratively. Access t educatin is universal, and higher educatin institutins n lnger differentiate themselves by hw many peple they reject yearly. Variability between students is expected and leveraged (利用) as yung peple teach ne anther and use their differences as a surce f strength. Shemsy naturally explres what she is curius abut at a pace she sets. She still has sme classes t take that are mandatry fr children glbally: Being Human and the Histry f Humanity.
We invite yu t think abut yur visin fr educatin in the year 2080, what des it lk like, wh des it serve,and hw des it transfrm ur scieties?
13．What des paragraph 1 mainly tell us?
A．There are different types f educatin.
B．The present educatin needs imprvements.
C．Educatin and ecnmy are clsely assciated.
D．The gal f future educatin is fundamentally different.
14．What d we knw abut the Learning Hub that Shemsy ges t?
A．It accepts students f all ages.B．It prmtes cmpetitin.
C．It discurages individualized learning.D．It is all abut play-based learning.
15．What des the underlined wrd “mandatry” in paragraph 3 mean?
A．Tugh.B．Satisfactry.C．Optinal.D．Required.
16．What is the suitable title fr the text?
A．An Example t AllB．A Visin fr Educatin
C．A Challenge fr EducatinD．A Jurney int the Future
（2024·浙江寧波·二模）Laughter cmes in many frms, frm a plite chuckle t an infectius hwl f amusement. Scientists are nw develping an AI system that can cpy varius frms f laughter accurately. The team behind the laughing rbt, Erica, say that the system culd imprve natural cnversatins between peple and AI systems.
Dr. Kji Inue, lead authr f the research frm Kyt University, highlights empathy (共情) as a crucial aspect f cnversatinal AI, suggesting laughter sharing as a means fr rbts t cnnect with users. T achieve this, Inue and his team gathered data frm ver 80 speed-dating dialgues between male students and Erica, initially perated by amateur actrs.
Dialgue data labeled fr individual, scial, and jyful laughter was used t train an AI system t identify and prduce fitting laughter respnses. Based n the audi files, the algrithm (算法) learned their subtle differences, aiming t imitate scial laughs subtly and hearty laughs empathetically.
“Our biggest challenge in this wrk was identifying the actual cases f shared laughter,” explained Inue, emphasizing the need fr careful categrizatin. Erica’s “sense f humr” was tested with fur dialgues, integrating the new shared-laughter algrithm. These were cmpared t cases where Erica didn’t laugh r emitted scial laughs upn detecting laughter.
The clips were played t 130 vlunteers wh rated the shared-laughter algrithm highly fr empathy and naturalness. The team believed laughter culd imbue rbts with unique character traits, including cnversatinal behavirs like laughter, eye gaze, gestures, and speaking style. Hwever, Inue acknwledged it culd take ver 20 years t have a “casual chat with a rbt like we wuld with a friend.”
Prfessr Sandra Wachter, f the Oxfrd Internet Institute at the University f Oxfrd, said, “One f the things I’d keep in mind is that a rbt r algrithm will never be able t understand yu. It desn’t understand the meaning f laughter. They fail t feel, but they might get very gd at making yu believe they understand what’s ging n.”
17．Why d scientists develp the AI system that can cpy varius frms f laughter?
A．T make rbts sund mre human-like.
B．T help rbts understand human emtins better.
C．T enable rbts t have a sense f humr like humans.
D．T enhance the emtinal interactin between peple and AI systems.
18．What was the challenge Inue faced while wrking n this prject?
A．Creating an algrithm that can genuinely feel amusement.
B．Identifying the situatins where laughter is truly understd.
C．Distinguishing between different types f laughter accurately.
D．Cllecting sufficient data fr training the machine learning system.
19．What des the underlined wrd “imbue” mean in Paragraph 5?
A．Equip.B．Inspire.C．Engage.D．Influence.
20．What is Prfessr Sandra Wachter’s view n laughing rbts?
A．They are nt capable f capturing human laughter.
B．They can imitate laughter but lack thrugh cmprehensin.
C．It is pssible fr them t play tricks n humans ccasinally.
D．It will take lng befre humans have cmfrtable cnversatins with them.
（2024·廣東佛山·二模）Building artificial intelligences that sleep and dream can lead t mre dependable mdels, accrding t researchers wh aim t mimic (模仿) the behavir f the human brain.
Cncett Spampinat and his research members at the University f Catania, Italy, were lking fr ways t avid a phenmenn knwn as “disastrus frgetting”, where an AI mdel trained t d a new task lses the ability t carry ut jbs it previusly excelled at. Fr instance, a mdel trained t identify animals culd learn t spt different fish species, but then might lse its ability t recgnize birds. They develped a methd f training AI called Wake-Sleep Cnslidated Learning (WSCL), which mimics the way that ur brains rerganize shrt-term memries f daily learning when we are asleep.
Besides the usual training fr the “awake” phase, mdels using WSCL are prgrammed t have perids f “sleep”, where they analyze awake data frm earlier lessns. This is similar t human sptting cnnectins and patterns while sleeping.
WSCL als has a perid f “dreaming”, which invlves nvel data made frm cmbining previus cncepts. This helps t integrate previus paths f digital “neurns (神經(jīng)元)”, freeing up space fr future cncepts. It als prepares unused neurns with patterns that will help them pick up new lessns mre easily.
The researchers tested three AI mdels using a traditinal training methd, fllwed by WSCL training. Then they cmpared perfrmances fr image identificatin. The sleep-trained mdels were 2 t 12 percent mre likely t crrectly identify the cntents f an image. They als measured an increase in hw much ld knwledge a mdel uses t learn a new task.
Despite the results, Andrew Rgyski at the University f Surrey, UK, says using the human brain as a blueprint isn’t necessarily the best way t bst AI perfrmance. Instead, he suggests mimicking dlphins, which can “sleep” with ne part f the brain while anther part remains active. After all, an AI that requires hurs f sleep isn’t ideal fr cmmercial applicatins.
21．WSCL was develped t help imprve AI’s ______.
A．reliabilityB．creativityC．securityD．ppularity
22．What d mdels using WSCL d during the “sleeping” perids?
A．Generate new data.B．Prcess previus data.
C．Receive data fr later analysis.D．Save data fr the “awake” phase.
23．What is paragraph 5 mainly abut?
A．The applicatin f WSCL.B．The benefits f AI research.
C．The findings f the research.D．The underlying lgic f WSCL.
24．Which best describes Andrew’s attitude twards the sleep-trained mdels?
A．Cautius.B．Prejudiced.C．Pessimistic.D．Uncncerned.
（2024·廣東韶關(guān)·二模）Wuld a persn brn blind, wh has learned t distinguish bjects by tuch, be able t recgnize them purely by sight if he regained the ability t see? The questin, knwn as Mlyneux’s prblem, is abut whether the human mind has a built-in cncept f shapes that is s inbrn that a blind persn culd immediately recgnize an bject with restred visin. Alternatively, the cncepts f shapes are nt inbrn but have t be learned by explring an bject thrugh sight, tuch and ther senses.
After their attempt t test it in blind children failed, Lars Chittka f Queen Mary University f Lndn and his team carried ut anther experiment n bumblebees. T test whether bumblebees can frm an internal representatin f bjects, they first trained the insects t distinguish glbes frm cubes using a sugar reward. The bees were first trained in the light, where they culd see but nt tuch the bjects. Then they were tested in the dark, where they culd tuch but nt see the items. The researchers fund that the insects spent mre time in cntact with the shape they had been trained t assciate with the sugar reward, even thugh they had t rely n tuch rather than sight t distinguish the bjects.
The researchers als did the ppsite test with untrained bumblebees, first teaching them with rewards in the dark and then testing them in the light. Again, the bees were able t recgnize the shape assciated with the sugar reward, thugh they had t rely n sight rather than tuch in the test. In shrt, bees have slved Mlyneux’s prblem because the fact suggests that they can picture bject features and access them thrugh sight r tuch.
Hwever, sme experts express their warning s against the result. Jnathan Birch, a philspher f science, cautins that the bees may have had prir experience assciating visual and tactile (觸覺) infrmatin abut straight edges and curved surfaces in their nests.
25．What is Mlyneux’s prblem abut?
A．Whether mankind’s sense f tuch utweighs sight.
B．Whether mankind’s idea f shape is inbrn r learned.
C．Whether blind peple can identify the shape f an item.
D．Whether the blind can regain their sense f tuch after recvery.
26．Hw did Lrs Chittka and his clleagues try t figure ut Mlyneux’s prblem?
A．By experimentatin n blind children.
B．By cnducting cntrlled experiments.
C．By rewarding bumblebees with sugar.
D．By bserving bumblebees in their nests.
27．What is Jnathan Birch’s attitude twards the cnclusin f the bee experiments?
A．Skeptical.B．Supprtive.
C．Dismissive.D．Ambiguus.
28．Which f the fllwing can be the best title f the passage?
A．Scientists Fund Senses Matter
B．Visual-Tactile Puzzle Has Been Slved
C．Experiments Will Help the Blind Regain Sight
D．Bumblebees May Help Slve Mlyneux’s Prblem
（2024·廣東梅州·二模）New findings suggest that when it-cmes t learning, the snake may be quite a bit like humans. David Hltzman, a scientist at the University f Rchester, has fund that snakes have a much greater capacity fr learning than earlier studies had indicated.
Hltzman’s study challenged 24 snakes t escape frm a black plastic cntainer the size f a child’s pl. Cards munted n the cntainer’s walls and tape n its flr prvided the snakes with visual and tuchable signals t find their gal: hles in the cntainer’s bttm that ffer a dark, cmfrtable spt t hide.
Simply falling int a hle isn’t the nly prf that the snakes are learning smething, thugh. “Speed t find that gal is ne f the measures which shws they’re learning,” Hltzman says. “On average, they take ver 700 secnds t find the crrect hle n the first day f training, and then g dwn t abut 400 secnds by the furth day f training. Sme are actually very fast and find it in less than 30 secnds.”
Studies dating back t the 1950s interpreted snakes’ awkwardness with mazes(迷宮)as a pr reflectin n their intelligence. “Early attempts t study snake intelligence were prblematic because the studies used mazes as testing arenas(場地)-as thugh snakes might be expected t run thrugh mazes in the same way mice run thrugh mazes,” says Peter Kareiva, a prfessr f zlgy. “Snakes d nt encunter anything like mazes in nature, and they d nt learn hw t run mazes in labratry cnditins.”
Hltzman als fund a few age-based differences in the signals the snakes use. Yung snakes appear t be mre adaptable and resurceful, using a variety f clues t find their way t the exit.But their elders seem t rely much mre heavily n visual clues. “Actually, ne f the amazing findings frm ur studies is that snakes d use visin in lcating places,” says Hltzman. “They dn’t just rely n the chemical clues picked up by sticking their tngues ut, as many snake bilgists assume.”
29．What is the functin f the cards and tape?
A．T direct the snakes t the exits.
B．T prtect the snakes frm bright lights.
C．T cver the hles at the cntainer’s bttm.
D．T make the cntainer a cmfrtable spt t stay.
30．What d the data in paragraph 3 shw abut the snakes accrding t Hltzman?
A．They are skillful escapers.B．They are gd learners.
C．They cmmunicate with each ther.D．They adapt t envirnments quickly.
31．What was the prblem with early attempts t study snake intelligence?
A．They chse the wrng testing arenas.
B．They failed t d tests in labratry cnditins.
C．They referred t studies dating back t the 1950s.
D．They cmpared snakes with a different kind f animal.
32．What astnishes Hltzman abut snakes?
A．They rely n sight t find their way.
B．They leave chemical clues everywhere.
C．The yung beat their elders in many ways.
D．Their tngues are unable t recgnize chemical clues.
（2024·江蘇南京·二模）“Anxiety.” The very wrd invites discmfrt. Its effects—shrtness f breath, punding heart, muscle tensin—are utright upsetting. But, as a clinician, I find that we tend t miss ut n many valuable pprtunities presented by this human emtin. In and f itself, anxiety is nt deadly, nr is it a disease. Quite the cntrary: it is an indicatr f brain and sensry health. Once we accept that it is a nrmal, thugh uncmfrtable, part f life, we can use it t help us.
We all knw wrking ut at the gym is hard. By nature, a “gd wrkut” is uncmfrtable, since it invlves pushing ur physical strength past what we can easily d. The sweet spt f exercise is always a smewhat challenging experience. Similarly, if yu want t be emtinally strnger, yu need t face sme tensin. Fr example, ne effective treatment fr fear is expsure therapy (療法), which invlves gradually encuntering things that make ne anxius, reducing fear ver time.
Humans are scial creatures. When my patients learn t pen up t their partners abut their anxieties, they almst always reprt a greater sense f emtinal clseness. Als, as internatinal relatinship expert Sue Jhnsn teaches, when we express ur need fr cnnectin during challenging mments (e.g., “I’m having a hard time right nw and culd really use yur supprt”), it creates greater cnnectin and turns ur anxiety int lve.
Frm time t time, we find urselves at the end f ur rpe. Our respnsibilities pile up, ur resurces break dwn, and we feel uncmfrtably anxius—what we’re experiencing is called stress. Simply put, the demands placed upn us utweigh ur available resurces, just like a set f scales (天平) ging ut f balance. Fcusing n wrk and pretending everything is OK nly leads t disastrus results. Medical treatment fr stress may functin fr a while, but it tends t make things wrse in the lng run. The nly slutin t deal with stress is t d the mathematics t balance the scales.
33．What des the authr say abut anxiety?
A．It is an invitatin t diseases.
B．It indicates stable mental health.
C．It csts us many valuable chances.
D．It is a natural emtinal expressin.
34．Why des the authr mentin “gd wrkut” in paragraph 2?
A．T prve hw exercise influences emtins.
B．T suggest an effective way t challenge limits.
C．T explain hw anxiety builds emtinal strength.
D．T shw a psitive cnnectin between mind and bdy.
35．What is paragraph 3 mainly abut?
A．The key t clseness is partners’ supprt.
B．Sharing anxieties imprves relatinships.
C．Humans are defined by their scial nature.
D．Expressing feelings keeps us ff anxieties.
36．Accrding t the last paragraph, hw can we deal with stress?
A．Devte mre energy t ur wrk.
B．Increase resurces available t us.
C．Seek prfessinal medical treatment.
D．Master advanced mathematical skills.
（2024·江蘇連云港·二模）The science f why insects gather arund lights at night has never been nailed dwn. Ppular theries prpse that mths and ther insects navigate (導(dǎo)航) by the mn and mistake lamps fr mnlight, r that the insects fly twards light t escape cming danger. Nw researchers believe they have a mre cnvincing answer: cntrary t current theries, insects are nt attracted t light frm far away, but becme trapped if they fly clse t an artificial light surce.
Accrding t Dr Sam Fabian, study c-authr and Imperial Cllege Lndn entmlgist, mths and many ther insects that fly at night evlved t tilt (傾斜) their backs t wherever is brightest. Fr hundreds f millins f years, this was the sky rather than the grund. The trick tld insects which way was up and ensured they flew level. But then came artificial lighting. Mths fund themselves tilting their backs t street lamps. This caused them t circle arund the lamps endlessly, the insects trapped by their evlutin.
Fabian and his clleagues filmed insect flight paths arund lights in the lab. The vides reveal that time and again, mths and dragnflies turned their backs t artificial lights, which appeared t greatly change their flight paths. If the light is abve them, they might start rbiting it, but if it’s behind them, they start tilting backwards and end up flying in circles r diving tward the grund.
Researchers have lng warned that light pllutin is a big driving frce in the dramatic decline in insect ppulatins. Mths and ther insects that becme trapped arund lamps becme easily caught by bats. The artificial lighting can als fl them int thinking it is daytime, causing them t bed dwn and skip a night’s feeding.
There are, Fabian believes, helpful lessns frm the research. “What this tells us is that the directin f artificial light matters. Culd we change lighting envirnments t nt trap insects? Fr we’re facing a massive decline in insects arund the wrld, and artificial light at night is ne f the factrs that culd ptentially be leading t this decline,” Fabian said.
37．What d the underlined wrds “nailed dwn” in paragraph 1 mean?
A．Ppularized widely.B．Discussed penly.
C．Defined accurately.D．Explred academically.
38．Fabian’s study fund that mths circle arund the lamps endlessly because ______.
A．they can’t keep their balance.
B．they use imprper flight attitude.
C．they lse track f which way is up.
D．they are attracted t lights frm far away.
39．What is the significance f the research finding?
A．It may lead t better cnservatin f insects.
B．Natural enemies f insects will be gt rid f.
C．Artificial lighting will be greatly reduced at night.
D．It may raise cncerns fr insects’ eating behavir.
40．What is the text mainly abut?
A．Why insects lse their ability t fly at night.
B．Why artificial light and evlutin trap insects.
C．Hw artificial light impacts insect ppulatins.
D．Hw insects evlved distinct strategies f flight.
（2024·江蘇連云港·二模）I’m a laypersn with a lve f science wh ccasinally reads science magazines. My apprach was frm an authr’s angle, spending mnths n research befre writing a single wrd fr Pig Heart By.
S where did I get the idea? Whenever I attend a schl event, that questin is asked. The answer is simple. Back in the mid 1990s, I read a newspaper article written by a dctr wh guessed that we wuld eventually have t turn t xentransplantatin (異種器官移植) as a pssible slutin t the lack f human rgan dnrs. It left my mind filled with questins. What are the cnsequences? D we really have the right t treat animals as me re rgan surces fr humans? S I headed t my nearest bkshp and bught all the bks I culd n heart transplants in particular.
I’ve fund questins are ne f the best places t start frm when writing a nvel. In my stry Camern, wh needs a heart transplant, knws he is unlikely t see his next birthday unless he receives ne, but he is a lng way dwn the waiting list. When a genetically mdified (GM) pig’s heart is ffered by a pineering dctr, Camern decides t g fr it —and his new heart cmpletely changes his life in unexpected ways.
Nw sme peple think that the subject matter is nt suitable fr children, criticizing the cruel and inhuman ways f xentransplantatin. I cmpletely disagree. As a children’s authr, it never ceases t amaze me hw sme adults underestimate what subject matter will interest and stimulate children. I wanted t write a stry that prvided n right r wrng answers, a stry that wuld allw the reader t walk in Camern’s shes fr a while and think abut what decisins they wuld make and hw they wuld react if they t were faced with his situatin.
Fictinal stries that explre new ideas when it cmes t STEM (science, technlgy, engineering and mathematics) subjects als have a part t play in enriching ur children’s reading and learning. Varius studies have shwn that reading fictin enhances ur children’s ability t grasp new cncepts. Pig Heart By was my attempt t incrprate science pssible int a believable, thught-prvking (令人深思的) stry.
41．Where did the authr get inspiratin frm t write Pig Heart By?
A．A schl event.B．A news item.
C．Science magazines.D．Bks n heart transplants.
42．What might be a majr cncern f thse wh disagree with Pig Heart By?
A．Animal rights.B．GM technlgy.
C．Organ transplant risks.D．Organ shrtage crisis.
43．What are the last tw paragraphs f the text mainly abut?
A．Ways f tapping children ‘s intelligence.B．Ptential applicatin f fictinal stries.
C．Supprting evidence fr justifying the bk.D．Influence f fictinal stries n STEM subjects.
44．What is Pig Heart By?
A．An authr prfile.B．A science fictin nvel.
C．A guidebk t xentransplantatin.D．An essay n writing children’s literature.
（2024·山東棗莊·二模）Even if yu haven’t held a cnversatin with Siri r Alexa, yu’ve likely encuntered a chatbt nline. They ften appear in a chat windw that pps up with a friendly greeting: Thank yu fr visiting ur site.Hw can I help yu tday? Depending n the site, the chatbt is prgrammed t respnd accrdingly and even ask fllw-up questins.
Chatbts are a frm f cnversatinal AI designed t simplify human interactin with cmputers. They are prgrammed t simulate human cnversatin and exhibit intelligent behavir that is equivalent t that f a human.
Chatbts cmmunicate thrugh speech r text. Bth rely n artificial intelligence technlgies like machine learning and natural language prcessing (NLP), which is a branch f artificial intelligence that teaches machines t read, analyze and interpret human language. This technlgy gives chatbts a baseline fr understanding language structure and meaning. NLP, in essence, allws the cmputer t understand what yu are asking and hw t apprpriately respnd.
With develpments in deep learning and reinfrcement learning, chatbts can interpret mre cmplexities in language and imprve the dynamic nature f cnversatin between human and machine. Essentially, a chatbt tries t match what yu’ve asked t an intent that it understands. The mre a chatbt cmmunicates with yu, the mre it understands and the mre it learns t cmmunicate like yu and thers with similar questins. Yur psitive respnses reinfrce its answers, and then it uses thse answers again.
Frm custmer service chatbts nline t persnal assistants in ur hmes,chatbts have started t enter ur lives. In almst every industry, cmpanies are using chatbts t help custmers easily navigate their websites, answer simple questins and direct peple t the relevant pints f cntact. Persnal assistants like Siri and Alexa are designed t respnd t a wide range f scenaris and queries, frm current weather and news updates t persnal calendars, music selectins and randm questins.
45．Why des the authr mentin Siri and Alexa in Paragraph 1?
A．T explain hw a chatbt wrks.B．T shw where t find a chatbt.
C．T give examples f chatbts.D．T cmpare different chatbts.
46．What is the basis f chatbts?
A．Language study.B．Data transmissin.
C．Scial interactin.D．Natural language prcessing.
47．What des the underlined wrd “reinfrce” in paragraph 4 mean?
A．Inspire.B．Strengthen.C．Organize.D．Match.
48．What is the last paragraph mainly abut?
A．The future trend f chatbts.B．The authr’s predictins.
C．The effects f chatbts.D．The applicatins f chatbts.
（2024·福建莆田·二模）The year is 1763, and a 7-year-ld Mzart is abut t set ff n a tur arund Eurpe that will jump-start the Mzart legend. Mzart had a trick up his sleeve. When the yung Mzart heard a nte played-any nte-he culd immediately identify exactly which nte it was. It was an ability nw we knw as “perfect pitch”, and it seemed t be an example f the mysterius gifts that yung geniuses had been brn with. But is that really s?
Over my years f studying experts in varius fields, like Mzart, I have fund that there’s n such thing as a predefined ability. Actually, thse peple all develp their abilities thrugh “deliberate practice”, a purpseful and systematic type f practice that makes it pssible fr them t d things they therwise culd nt. In them, ptential is an expandable vessel, shaped by the varius things they d thrughut their lives.
One f my testimnies came frm Ray Allen, a ten-time All-Star in the NBA．Allen’s jump sht was nt nticeably better than his teammates’ back in high schl; in fact, it was pr. But with hard wrk and dedicatin, he transfrmed his jump sht int ne s graceful and natural that peple assumed he was brn with it.
But it desn’t mean “Just keep wrking at it, and yu’ll get there”. Heartfelt desire and hard wrk alne will nt lead t imprved perfrmance. The right srt f practice carried ut ver a sufficient perid f time will lead t imprvement. Nthing else. And this is true whether ur gal is t becme a cncert pianist r just play the pian well enugh t amuse urselves, t be the greatest three-pint shter r just build urselves up. Deliberate practice is the gld standard fr anyne in any field wh wishes t build new skills and abilities.
49．Why is Mzart’s perfect pitch mentined in paragraph 1?
A．T intrduce an inbrn talent.B．T explain reasns fr success,
C．T lead t reflectin n gifts.D．T define a brilliant trick.
50．Which is the mst imprtant in making an expert accrding t the writer?
A．Affectin.B．Experience.C．Training.D．Gifts.
51．What des the underlined wrd “testimnies” mean in paragraph 3?
A．Challenges.B．Prfs.C．Cmments.D．Puzzles.
52．Which f the fllwing is the best title fr the text?
A．Secret f Great TalentsB．Bm t Stand Out
C．A Surprising DiscveryD．Start When Yung

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